| Title: | Atrial fibrillation prediction by combining ECG markers and CMR radiomics |
| Journal: | Scientific Reports |
| Published: | 7 Nov 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36344532/ |
| DOI: | https://doi.org/10.1038/s41598-022-21663-w |
| URL: | https://www.nature.com/articles/s41598-022-21663-w.pdf |
| Citations: | 7 (7 in last 2 years) as of 8 Aug 2024 |
Publication 10671
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia. It is associated with a higher risk of important adverse health outcomes such as stroke and death. AF is linked to distinct electro-anatomic alterations. The main tool for AF diagnosis is the Electrocardiogram (ECG). However, an ECG recorded at a single time point may not detect individuals with paroxysmal AF. In this study, we developed machine learning models for discrimination of prevalent AF using a combination of image-derived radiomics phenotypes and ECG features. Thus, we characterize the phenotypes of prevalent AF in terms of ECG and imaging alterations. Moreover, we explore sex-differential remodelling by building sex-specific models. Our integrative model including radiomics and ECG together resulted in a better performance than ECG alone, particularly in women. ECG had a lower performance in women than men (AUC: 0.77 vs 0.88, p < 0.05) but adding radiomics features, the accuracy of the model was able to improve significantly. The sensitivity also increased considerably in women by adding the radiomics (0.68 vs 0.79, p < 0.05) having a higher detection of AF events. Our findings provide novel insights into AF-related electro-anatomic remodelling and its variations by sex. The integrative radiomics-ECG model also presents a potential novel approach for earlier detection of AF.</p>
11 Authors
- Esmeralda Ruiz Pujadas
- Zahra Raisi-Estabragh
- Liliana Szabo
- Cristian Izquierdo Morcillo
- Víctor M. Campello
- Carlos Martin-Isla
- Hajnalka Vago
- Bela Merkely
- Nicholas C. Harvey
- Steffen E. Petersen
- Karim Lekadir
1 Application
| Application ID | Title |
|---|---|
| 2964 | Description of cardiovascular phenotype in the UK Biobank population based on cardiovascular magnetic resonance and carotid ultrasound |
Enabling scientific discoveries that improve human health